Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

2022 ◽  
pp. 1118-1129
Author(s):  
Nawaf N. Hamadneh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Level ◽  
Firefly Algorithm ◽  
Sea Water ◽  
Dead Sea ◽  
Water Levels ◽  
Artificial Neural ◽  
The Dead ◽  
Predicted Values

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

2020 ◽  
Vol 11 (3) ◽  
pp. 19-29
Author(s):  
Nawaf N. Hamadneh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Level ◽  
Firefly Algorithm ◽  
Sea Water ◽  
Statistical Tests ◽  
Dead Sea ◽  
Water Levels ◽  
Artificial Neural ◽  
The Dead

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

2010 ◽  
Vol 36 (5) ◽  
pp. 620-627 ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Rahman Khatibi ◽  
Ali Aytek ◽  
Oleg Makarynskyy ◽  
Jalal Shiri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Water Level ◽  
Sea Water ◽  
Artificial Neural

Gypsum saturation degrees and precipitation potentials from Dead Sea - seawater mixtures

2009 ◽  
Vol 6 (5) ◽  
pp. 416 ◽  
Author(s):  
Itay J. Reznik ◽  
Jiwchar Ganor ◽  
Assaf Gal ◽  
Ittai Gavrieli
Keyword(s):  
Surface Water ◽  
Water Level ◽  
Water Column ◽  
Sea Water ◽  
Theoretical Calculations ◽  
Dead Sea ◽  
Gypsum Precipitation ◽  
The Dead ◽  
Rate Of Decline ◽  
The 1960S

Environmental context. Since the 1960s the Dead Sea water level has dropped by nearly 30 m and over the last decade the rate of decline accelerated to over 1 m per year. Conveying seawater to the Dead Sea to stabilise or even raise its water level is currently being considered but may result in ‘whitening’ of the surface water through the formation of minute gypsum crystals that will remain suspended in the water column for a prolonged period of time. This paper is a first step in attaining the relevant physical and chemical parameters required to assess the potential for such whitening of the Dead Sea. Abstract. Introduction of seawater to the Dead Sea (DS) to stabilise its level raises paramount environmental questions. A major concern is that massive nucleation and growth of minute gypsum crystals will occur as a result of mixing between the SO42–-rich Red Sea (RS) water and Ca2+-rich DS brine. If the gypsum will not settle quickly to the bottom it may influence the general appearance of the DS by ‘whitening’ the surface water. Experimental observations and theoretical calculations of degrees of saturation with respect to gypsum (DSG) and gypsum precipitation potentials (PPT) were found to agree well, over the large range but overall high ionic strength of DS–RS mixtures. The dependency of both DSG and PPT on temperature was examined as well. Based on our thermodynamic insights, slow discharge of seawater to the DS will result in a relatively saline upper water column which will lead to enhanced gypsum precipitation.

scholarly journals MODEL PREDIKSI LEVEL AIR DI LAHAN PERKEBUNAN KELAPA SAWIT DENGAN JARINGAN SARAF TIRUAN BERDASARKAN PENGUKURAN SENSOR RAIN GAUGE DAN ULTRASONIK

2021 ◽  
Vol 10 (1) ◽  
pp. 104
Author(s):  
Hasan Al Banna ◽  
Bayu Dwi Apri Nugroho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Water Level ◽  
Rain Gauge ◽  
Water Levels ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Artificial Neural ◽  
Water Level Prediction

Monitoring and regulating water levels in oil palm swamps has an essential role in providing sufficient water for crops and conserving the land to not easily or quickly deteriorate. Presently, water level is still manual and has weaknesses, one of which is the accuracy of the data taken depending on the observer. Technology such as sensors integrated with artificial neural network is expected to observe and regulate water levels. This study aims to build a prediction model of water levels in oil palm plantations with artificial neural networks based on the rain gauge and ultrasonic sensors installed on Automatic Weather Station (AWS). The obtained results showed that the prediction model runs well with an R2 value of 0,994 and RMSE 1,16 cm. The water level prediction model in this research then tested for accuracy to prove the model's success rate. Testing the water level prediction model's accuracy in the dry season obtained an R2 value of 0,96 and an RMSE of 1,99 cm. Testing the water level prediction model's accuracy in the rainy season obtained an R2 value of 0,85 and an RMSE value of 4,2 cm. Keywords : artificial neural network, automatic weather station, palm oil, water level

scholarly journals Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
João Paulo Lyra Fialho Brêda ◽  
Rodrigo Cauduro Dias de Paiva ◽  
Olavo Corrêa Pedrollo ◽  
Otávio Augusto Passaia ◽  
Walter Collischonn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Balance ◽  
Water Level ◽  
Integrated Approach ◽  
Water Levels ◽  
Ann Model ◽  
Storage Reservoirs ◽  
Ann Models ◽  
Artificial Neural

ABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW.

scholarly journals Observations of positive sea surface temperature trends in the steadily shrinking Dead Sea

2018 ◽  
Author(s):  
Pavel Kishcha ◽  
Rachel T. Pinker ◽  
Isaac Gertman ◽  
Boris Starobinets ◽  
Pinhas Alpert
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Water ◽  
Dead Sea ◽  
Water Levels ◽  
Sea Surface ◽  
Surface Warming ◽  
Temperature Trends ◽  
The Dead

Abstract. The steadily shrinking Dead Sea followed by sea surface warming compensates surface water cooling due to increasing evaporation, and even causes the observed positive Dead Sea surface temperature trends. Using observations from Moderate Resolution Imaging Spectroradiometer (MODIS), positive trends were detected in both daytime (0.06 °C year−1) and nighttime (0.04 °C year−1) Dead Sea surface temperature (SST) over the period of 2000–2016. These positive SST trends were observed in the absence of positive trends in surface solar radiation measured by the Dead Sea buoy pyranometer. Neither changes in water mixing in the Dead Sea nor changes in evaporation could explain surface temperature trends. There is a positive feedback loop between the shrinking of the Dead Sea and positive SST trends, which leads to the accelerating decrease in Dead Sea water levels during the period under study. Note that there are two opposite processes based on available measurements: on the one hand, the measured accelerating rate of Dead Sea water levels suggests a long-term increase in Dead Sea evaporation which is expected to be accompanied by a long-term decrease in sea surface temperature. On the other hand, the positive feedback loop leads to the observed shrinking of the Dead Sea area followed by sea surface warming year on year. The total result of these two opposite processes is the statistically significant positive sea surface temperature trends in both daytime (0.06 °C year−1) and nighttime (0.04 °C year−1) during the period under investigation, observed by the MODIS instrument. Our results shed light on the continuing hazard to the Dead Sea and possible disappearance of this unique site.

scholarly journals USE OF ARTIFICIAL NEURAL NETWORKS TO PREDICT TERRITORIAL ECONOMIC INDICATORS

2019 ◽  
Vol 4 (8) ◽  
pp. 143-146
Author(s):  
Gocha Ugulava
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Economic Indicators ◽  
Regional Economic ◽  
The Neural Network ◽  
Artificial Neural ◽  
Predicted Values

Modern economic science is unthinkable without predicting and planning the prospects for economic life development. There are many different mathematical and statistical tools in the arsenal of scientists as well as practitioners and economists today in purpose of forecasting. To date, one of the most prominent effective tools for data analytics is artificial neural networks. Artificial Neural Network - is a mathematical mod- el created in the likeness of a human neural network, and its software and hardware implementation. We carried out modeling and forecasting of regional economic indicators using the artificial neural network of the three-layer perceptron architecture. The network architecture and neuron settings were automatically formatted through the programming language R and its package - Neuralnet. During the forecasting phase, the data vectors were presented as data frame in five input parameters (DFI, FAI, EMP, BT, CPI), according to the neural network forecast of the regional gross domestic product (RGDP_NN) was calculated. All data are from the Imereti region and are taken from official GeoStat sources. Forecasting was done at the same time scale (2006-2017) to enable us to compare the predicted values with the actual ones to verify the level of fore- cast accuracy. We also tested the results of the neural network in another way - compared to the predicted values using multiple linear regression on the same data. The accuracy of the predicted values calculated by the neural network was quite high, which was not declining but slightly ahead of the accuracy coefficients of the predicted values obtained through linear regression. Also, the predictive values calculated by the neural network with high adequacy and accuracy were compared with actual, existing ones. Presented material shows that the use of artificial neural networks for the prediction of territorial economic indicators is reasonable and justified. Their role in analyzing and predicting indicators that are characterized by nonstationarity, dynamism, lack of a definite trend, periodicity, nonlinear structure is especially increased. It is therefore advisable to apply this method in regional economic studies, in predicting territorial development plans, strategies, targets and indicators.

scholarly journals Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Nawaf N. Hamadneh ◽  
Muhammad Tahir ◽  
Waqar A. Khan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mean Squared Error ◽  
Prediction Errors ◽  
Metaheuristic Algorithm ◽  
Squared Error ◽  
Ann Models ◽  
Simulation Results ◽  
Artificial Neural ◽  
Predicted Values

The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.

Prediction of Hardenability of Gear Steel Using Stepwise Polynomial Regression and Artificial Neural Network

2010 ◽  
Vol 118-120 ◽  
pp. 332-335
Author(s):  
Xiu Hua Gao ◽  
Tian Yong Deng ◽  
Hao Ran Wang ◽  
Chun Lin Qiu ◽  
Ke Min Qi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Polynomial Regression ◽  
Gear Steel ◽  
Artificial Neural ◽  
Predicted Values ◽  
Polynomial Regression Model

The prediction of the hardenability of gear steel has been carried using stepwise polynomial regression and artificial neural networks (ANN). The software was programmed to quantitatively predict the hardenability of gear steel by its chemical composition using two calculating models respectively. The prediction results using artificial neural networks have more precise than the stepwise polynomial regression model. The predicted values of the ANN coincide well with the actual data. So an important foundation has been laid for prediction and controlling the production of gear steel.

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